Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

Li, Jiayue; Piao, Yan

doi:10.3390/app13179528

Cited by 3 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the conventional multiple-object tracking (MOT) approach, the data association issues are converted into different challenges [5]. The MOT is categorized based on the usage of video sequences: offline and online tracking [6]. Online video processing systems incorporate deep and machine learning-based image processing models, which are among recent data processing techniques [7].…”

Section: Introductionmentioning

confidence: 99%

ResNet50-deep affinity network for object detection and tracking in videos

2024

IJATEE

View full text Add to dashboard Cite

Multiple-object tracking (MOT) plays a crucial role in addressing many fundamental challenges within the fields of computer vision and video analysis. The majority of MOT methods rely on two primary processes: object detection and data association. Initially, each video frame is analyzed to detect objects, followed by a subsequent step that establishes correlations among the detected objects across multiple frames to generate their tracks. However, the data association for tracking often relies on manually defined criteria such as motion, appearance, grouping, and spatial proximity, among others. In the study, the ResNet50-deep affinity network (DAN) was introduced, which had been designed for the detection and tracking of objects in videos, including those that appear and disappear between frames. The proposed method was evaluated using the widely recognized MOT17 dataset to address MOT challenges. During the preprocessing phase, photometric distortion correction, frame expansion, and cropping were performed. The ResNet50 model was utilized to extract features. The DAN was employed to identify object appearances in video frames and to calculate their cross-frame affinities (CFA). The approach was compared with existing research, including DAN, ByteTrack, the graph neural network for simultaneous detection and tracking (GSDT), the reptile search optimization algorithm with deep learning-based multiple object detection and tracking (RSOADL-MODT), the center graph network (CGTracker), the hybrid motion model, FlowNet2-deep learning, and the super chained tracker (SCT), to validate the efficiency of theResNet50-DAN method. The ResNet50-DAN method achieved superior results, with a multiple-object tracking accuracy (MOTA) of 84.2%, an F1 score for identification metrics (IDF1) of 80.3%, 10,352 false positives (FP), and 1,284 identity switches (ID-Sw). The ResNet50-DAN method demonstrated higher MOTA compared to the existing approaches, including DAN, ByteTrack, GSDT, RSOADL-MODT, CGTracker, the hybrid motion model, FlowNet2-DL, and SCT.

show abstract

Section: Introductionmentioning

confidence: 99%

ResNet50-deep affinity network for object detection and tracking in videos

2024

IJATEE

View full text Add to dashboard Cite

show abstract

Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching

Cui,

Dai,

Duan

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Addressing the challenges in online multi-object tracking algorithms under complex scenarios, where the independence among feature extraction, object detection, and data association modules leads to both error accumulation and the difficulty of maintaining visual consistency for occluded objects, we have proposed an end-to-end multi-object tracking method based on hypergraph matching (JDTHM). Initially, a feature extraction and object detection module is introduced to achieve preliminary localization and description of the objects. Subsequently, a deep feature aggregation module is designed to extract temporal information from historical tracklets, amalgamating features from object detection and feature extraction to enhance the consistency between the current frame features and the tracklet features, thus preventing identity swaps and tracklet breaks caused by object detection loss or distortion. Finally, a data association module based on hypergraph matching is constructed, integrating with object detection and feature extraction into a unified network, transforming the data association problem into a hypergraph matching problem between the tracklet hypergraph and the detection hypergraph, thereby achieving end-to-end model optimization. The experimental results demonstrate that this method has yielded favorable qualitative and quantitative analysis results on three multi-object tracking datasets, thereby validating its effectiveness in enhancing the robustness and accuracy of multi-object tracking tasks.

show abstract

Separable CenterNet Detection Network Based on MobileNetV3—An Optimization Approach for Small-Object and Occlusion Issues

Jiao,

Dong,

Diao

2024

Mathematics

View full text Add to dashboard Cite

This paper proposes a novel object detection method to address the challenges posed by small objects and occlusion in object detection. This work is performed within the CenterNet framework, leveraging the MobileNetV3 backbone to model the input image’s abstract representation in a lightweight manner. A sparse convolutional skip connection is introduced in the bottleneck of MobileNetV3, specifically designed to adaptively suppress redundant and interfering information, thus enhancing feature extraction capabilities. A Dual-Path Bidirectional Feature Pyramid Network (DBi-FPN) is incorporated, allowing for high-level feature fusion through bidirectional flow and significantly improving the detection capabilities for small objects and occlusions. Task heads are applied within the feature space of multi-scale information merged by DBi-FPN, facilitating comprehensive consideration of multi-level representations. A bounding box-area loss function is also introduced, aimed at enhancing the model’s adaptability to object morphologies and geometric distortions. Extensive experiments on the PASCAL VOC 2007 and MS COCO 2017 datasets validate the competitiveness of our proposed method, particularly in real-time applications on resource-constrained devices. Our contributions offer promising avenues for enhancing the accuracy and robustness of object detection systems in complex scenarios.

show abstract

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

Cited by 3 publications

References 46 publications

ResNet50-deep affinity network for object detection and tracking in videos

ResNet50-deep affinity network for object detection and tracking in videos

Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching

Separable CenterNet Detection Network Based on MobileNetV3—An Optimization Approach for Small-Object and Occlusion Issues

Contact Info

Product

Resources

About